Predicting Defective Engines using Convolutional Neural Networks on Temporal Vibration Signals


Nikou Günnemann, Jürgen Pfeffer ;
Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:92-102, 2017.


This paper addresses for the first time the problem of engines’ damage prediction using huge amounts of imbalanced data from "structure borne noise" signals related to the internal engine excitation. We propose the usage of a convolutional neural network on our temporal input signals, subsequently combined with additional static features. Using informative mini batches during training we take the imbalance of the data into account. The experimental results indicate good performance in detecting the minority class on our large real-world use case.

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